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[2602.15510] On the Geometric Coherence of Global Aggregation in Federated GNN
Summary
This paper discusses the geometric coherence issues in global aggregation for Federated Graph Neural Networks (GNNs) and proposes a new framework, GGRS, to enhance message-passing consistency across heterogeneous client updates.
Why It Matters
As federated learning becomes increasingly important for privacy-preserving machine learning, understanding the challenges of aggregating heterogeneous data is crucial. This research addresses a significant gap in ensuring that global models maintain relational integrity, which is vital for effective graph-based learning applications.
Key Takeaways
- Federated GNNs face challenges due to heterogeneous client graph structures.
- Standard aggregation methods can lead to degraded performance in global models.
- The proposed GGRS framework regulates updates based on geometric criteria to maintain coherence.
- GGRS enhances the stability of message passing without accessing client data.
- Experiments demonstrate the effectiveness of GGRS in preserving global coherence.
Computer Science > Machine Learning arXiv:2602.15510 (cs) [Submitted on 17 Feb 2026] Title:On the Geometric Coherence of Global Aggregation in Federated GNN Authors:Chethana Prasad Kabgere, Shylaja SS View a PDF of the paper titled On the Geometric Coherence of Global Aggregation in Federated GNN, by Chethana Prasad Kabgere and 1 other authors View PDF HTML (experimental) Abstract:Federated Learning (FL) enables distributed training across multiple clients without centralized data sharing, while Graph Neural Networks (GNNs) model relational data through message passing. In federated GNN settings, client graphs often exhibit heterogeneous structural and propagation characteristics. When standard aggregation mechanisms are applied to such heterogeneous updates, the global model may converge numerically while exhibiting degraded relational this http URL work identifies a geometric failure mode of global aggregation in Cross- Domain Federated GNNs. Although GNN parameters are numerically represented as vectors, they encode relational transformations that govern the direction, strength, and sensitivity of information flow across graph neighborhoods. Aggregating updates originating from incompatible propagation regimes can therefore introduce destructive interference in this transformation this http URL leads to loss of coherence in global message passing. Importantly, this degradation is not necessarily reflected in conventional metrics such as loss or this http URL address thi...
